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news-summarization-model.ipynb (31299, 2023-08-13)
news_summary.csv (11896415, 2023-08-13)

# News Summarization using Pre-trained Language Models This project aims to automatically generate concise and coherent summaries of news articles using pre-trained Large Language Models (LLMs). By utilizing powerful language models like T5 and GPT-3, we can extract the most important information from lengthy news articles and present it in a condensed form. ## Table of Contents - [Project Overview](https://github.com/Vivek-Kumar-9554/News-Summarization-LLM-Model/blob/master/#project-overview) - [Technologies Used](https://github.com/Vivek-Kumar-9554/News-Summarization-LLM-Model/blob/master/#technologies-used) - [How to Use](https://github.com/Vivek-Kumar-9554/News-Summarization-LLM-Model/blob/master/#how-to-use) - [Project Impact](https://github.com/Vivek-Kumar-9554/News-Summarization-LLM-Model/blob/master/#project-impact) - [Author](https://github.com/Vivek-Kumar-9554/News-Summarization-LLM-Model/blob/master/#author) ## Project Overview The goal of this project is to create a user-friendly interface where users can input news articles, and the system will provide a summarized version of the article as output. We achieve this through the following steps: 1. **Data Collection:** We collect a dataset of news articles and their corresponding summaries (I used a dataset from Kaggle). These articles are then preprocessed and tokenized to make them suitable for input to the language model. 2. **Fine-tuning the Model:** We fine-tune a pre-trained Large Language Model 'T5' using the collected dataset. This process involves training the model on the task of summarization to adapt it specifically for generating news summaries. 3. **User Interface:** We create a user interface using the Gradio library, allowing users to instantly input news articles and receive summarized versions. 4. **Summarization:** When a user inputs a news article, the fine-tuned language model generates a summary by identifying the text's key information and main points. 5. **Output:** The generated summary is presented to the user, providing them with a concise overview of the article's content. ## Technologies Used - Python - PyTorch (or TensorFlow, depending on the LM used) - Transformers Library (for pre-trained models) - Gradio (for creating the user interface) ## How to Use 1. Install the required libraries by running `pip install transformers gradio`. 2. Run the provided code to fine-tune the language model and create the Gradio interface. 3. Access the user interface by opening the provided link in your web browser. 4. Input a news article into the provided text box and click the "Summarize" button. 5. View the summarized version of the article provided by the system. ## Project Impact This project makes news consumption more efficient by providing users with concise summaries, allowing them to quickly understand the content of news articles without having to read through lengthy texts. It can be particularly useful for staying updated on various news topics in a time-effective manner. Feel free to contribute, modify, or expand upon this project to explore different pre-trained language models and further enhance the summarization capabilities. ## Author Vivek Kumar Contact: vivekvivekvivekvi@gmail.com GitHub: [Vivek-Kumar-9554](https://github.com/Vivek-Kumar-9554/News-Summarization-LLM-Model/blob/master/https://github.com/Vivek-Kumar-9554) --- *Disclaimer: This project is developed for educational purposes and may require fine-tuning or customization for specific use cases.*

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